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DATA ANALYTICS (B69)  (Dept. Info)Business  (Policies)
B69 DAT 8565Deep Learning for Business Analytics1.5 Units
Description:Deep learning has become a core skillset required to solve business problems in the unstructured, data-rich business world. Experts estimate approximately that 90% of the data in organizations is in the form of unstructured datasets, including images, texts, customer reviews, videos, and so on. Organizations would like to use these datasets to improve their business. Moreover, deep learning has a significant advantage over other machine learning algorithms in that it does not require extracting "features" manually prior to applying algorithms. Leading-edge organizations are also expecting business analysts and managers to be familiar with applying deep learning models to solve business problems using unstructured data. This course is recommended but is not required for MS-Business Analytics (MSA) students. It will teach students to build deep learning models for solving business problems using Python libraries (e.g., Keras, Tensorflow). We will cover a range of algorithms from neural networks foundations to convolutional and recurrent network structures; these will be applied in domains such as marketing, customer behavior, and predicting finance risks. Students will better understand the practical use of deep learning with the use of the following five questions: (1) How can unstructured datasets be visualized and analyzed? (2) What are neural networks, and how can they be optimized? (3) What is the deep learning model, and how can it be used in business? (4) Which deep learning structure should be used for a given business problem? (5) How can a deep learning model be developed to solve business problems? In summary, the course will expose students to prevalent business applications of deep learning in different domains (e.g., customer analytics, supply chain analytics, healthcare analytics, financial technology analytics, accounting analytics, talent analytics). Upon completing this course, students will know how to build and optimize deep learning models for dif
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Instruction Type:Online Course Grade Options:CP Fees:
Course Type:HomeSame As:N/AFrequency:None / History
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